基于忆阻器的S-分布时滞随机神经网络的均方指数稳定性

doi:10.6040/j.issn.1671-9352.0.2015.545

山东大学学报（理学版） ›› 2016, Vol. 51 ›› Issue (5): 130-135.doi: 10.6040/j.issn.1671-9352.0.2015.545

基于忆阻器的S-分布时滞随机神经网络的均方指数稳定性

王长弘,王林山^*

中国海洋大学数学科学学院, 山东青岛 266100

收稿日期:2015-11-18 出版日期:2016-05-20 发布日期:2016-05-16
通讯作者: 王林山(1955— ),男,教授,博导,研究方向为动力系统与人工神经网络. E-mail:wangls@ouc.edu.cn E-mail:wchhty@qq.com
作者简介:王长弘(1990— ),男,硕士研究生,研究方向为动力系统与人工神经网络. E-mail:wchhty@qq.com
基金资助:
国家自然科学基金资助项目(11171374);山东省自然科学基金重点资助项目(ZR2011AZ001)

Mean square exponential stability of memristor-based stochastic neural networks with S-type distributed delays

WANG Chang-hong, WANG Lin-shan^*

School of Mathematical Science, Ocean University of China, Qingdao 266100, Shandong, China

Received:2015-11-18 Online:2016-05-20 Published:2016-05-16

摘要/Abstract

摘要： 利用Lyapunov泛函方法及随机分析技巧,研究了基于忆阻器的S-分布时滞随机神经网络的适定性和均方指数稳定性问题,给出了稳定性判据,并仿真检验了结果的有效性。

关键词: 随机分析, Lyapunov泛函, 忆阻器神经网络, S-分布时滞, 均方指数稳定

Abstract: The mean square exponential stability of a class of memristor-based stochastic neural networks with S-type distributed delays is investigated. A criterion for the stability is given by using Lyapunov functional method and stochastic analysis technique. An example is given to show the feasibility of the theoretical results.

Key words: memristor-based neural networks, S-type distributed delays, Lyapunov functional, exponential stability in mean square, stochastic analysis

中图分类号:

O175

王长弘,王林山. 基于忆阻器的S-分布时滞随机神经网络的均方指数稳定性[J]. 山东大学学报（理学版）, 2016, 51(5): 130-135.

WANG Chang-hong, WANG Lin-shan. Mean square exponential stability of memristor-based stochastic neural networks with S-type distributed delays[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(5): 130-135.

参考文献

[1] CHUA L O. Memristor-the missing circuit element[J]. IEEE Transactions on Circuit Theory, 1971, 18(5):507-519.
[2] STRUKOV D, SNIDER G, STEWART D, et al.The missing memristor found[J]. Nature, 2008, 453(7191):80-83.
[3] WILLIAMS R S. How we found the missing memristor[J]. IEEE Spectrum, 2008, 45(12):28-35.
[4] HU Jin, WANG Jun.Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays[C]. IJCNN 2010: The 2010 International Joint Conference on Neural Networks. 2010.
[5] WU Ailong, WEN Shiping, ZENG Zhigang. Synchronization control of a class of memristor-based recurrent neural networks[J]. Information Sciences, 2012, 183(1):106-116.
[6] WU Ailong, ZENG Zhigang. Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays[J]. Neural Networks, 2012, 36:1-10.
[7] WU Ailong, ZENG Zhigang. Exponential stabilization of memristive neural networks with time delays[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(12):1919-1929.
[8] CHEN Jiejie, ZENG Zhigang, JIANG Ping. Global exponential almost periodicity of a delayed memristor-based neural networks[J]. Neural Networks, 2014, 60:33-43.
[9] 王林山. 时滞递归神经网络[M]. 北京: 科学出版社, 2008. WANG Linshan. Recurrent neural networks with delays[M]. Beijing: Science Press, 2008.
[10] WANG Linshan, XU Daoyi. Global asymptotic stability of bidirectional associative memory neural networks with S-type distributed delays[J]. International Journal of Systems Science, 2002, 33(11):869-877.
[11] WEI Fengying, WANG Ke. The existence and uniqueness of the solution for stochastic functional differential equations with infinite delay[J]. Journal of Mathematical Analysis and Applications, 2007, 331(1):516-531.
[12] 甄艳,王林山. S-分布时滞随机广义细胞神经网络的均方指数稳定性分析[J]. 山东大学学报(理学版), 2014, 49(12):60-65. ZHEN Yan, WANG Linshan. Mean square exponential stability analysis of stochastic generalized cellular neural networks with S-type distributed delays[J]. Journal of Shandong University(Natural Science), 2014, 49(12):60-65.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于忆阻器的S-分布时滞随机神经网络的均方指数稳定性

Mean square exponential stability of memristor-based stochastic neural networks with S-type distributed delays

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

多维度评价

本文评价

推荐阅读 0

[1]	甄艳, 王林山. S-分布时滞随机广义细胞神经网络的均方指数稳定性分析[J]. 山东大学学报（理学版）, 2014, 49(12): 60-65.
[2]	常青, 周立群*. 一类具时滞细胞神经网络的全局一致渐近稳定性[J]. J4, 2012, 47(8): 42-49.
[3]	张伟伟1,王林山2*. S-分布时滞随机区间细胞神经网络的全局指数鲁棒稳定性[J]. J4, 2012, 47(3): 87-92.
[4]	张若军,王林山 . S-分布时滞区间细胞神经网络的全局渐近鲁棒稳定性[J]. J4, 2007, 42(2): 39-42 .
[5]	朱庆峰,石玉峰 . 局部Lipschitz条件下的正倒向重随机微分方程[J]. J4, 2007, 42(12): 58-62 .